Course

Complete ETL & Data Quality Testing — SQL, Cloud & AI

A complete ETL/Data Quality Test Engineer program: data warehousing and ETL fundamentals, advanced SQL for reconciliation, ETL testing types and techniques, Informatica/Talend/SSIS, cloud and big data ETL testing (Snowflake, Azure Data Factory, AWS Glue, Spark), Python-based ETL test automation, BI report and dashboard testing, and AI-augmented data validation — choose from 6 flexible packages, each with mock-interview checkpoints and full career-launch support.

⏱ 11 Weeks 🖥 Online 💻 4 Projects ⭐ 4.7/5
🎓 Book Free Demo 📞 +91 75502 51906
INCLUDED AT NO COST

Everything below is included at no extra cost

🚀 FREE!
Live Project(s)
💼 FREE!
Job Reference(s)
🎓 FREE!
Career Guidance
♾️ FREE!
Life Time Support
PACKAGES

We Offer Responsive Packages With An Unexplored Syllabus

No prior ETL needed
⏱ 5 Weeks
★ 4.7/5

ETL Testing Foundations

Data warehousing concepts and the advanced SQL that every ETL tester needs on day one.
  • Data Warehousing & ETL Fundamentals
  • Advanced SQL for ETL Testing
  • ETL Testing Types & Techniques
  • Comprehensive Interview Prep & Career Launch
SQL, sample data warehouse schema
View Curriculum →
Most enrolled
⏱ 5.5 Weeks
★ 4.8/5

ETL Tool Specialist (Informatica/Talend/SSIS)

On-prem ETL tooling still common across large enterprises, BFSI and insurance.
  • SQL for ETL Testing
  • Informatica, Talend & SSIS
  • ETL Testing Types & Techniques
  • Comprehensive Interview Prep & Career Launch
Informatica PowerCenter, Talend, SSIS, SQL
View Curriculum →
Cloud-native
⏱ 5 Weeks
★ 4.8/5

Cloud & Big Data ETL Testing

Snowflake, Azure Data Factory, AWS Glue, Spark and pipeline observability — where new ETL testing roles are actually being created.
  • SQL for ETL Testing
  • Big Data & Cloud ETL Testing
  • Observability & OpenTelemetry for Pipeline Testing
  • Comprehensive Interview Prep & Career Launch
Snowflake, Azure Data Factory, AWS Glue, Hive, Spark, OpenTelemetry, Grafana
View Curriculum →
Only at Fourcup
⏱ 4 Weeks
★ 4.7/5

ETL Test Automation & AI-Augmented Validation

Python-based reconciliation automation and AI-assisted validation — beyond manual SQL comparisons.
  • Advanced SQL Recap
  • ETL Test Automation
  • AI-Augmented Data Validation
  • Comprehensive Interview Prep & Career Launch
Python, pandas, SQL, ChatGPT/Claude
View Curriculum →
Flagship
⏱ 12 Weeks
★ 4.9/5

Complete ETL & Data Quality Flagship

Every module — SQL, tools, cloud/big data, automation, BI reporting, observability and AI — the full data-quality career path.
  • All 9 core modules, start to finish
  • On-prem tools + cloud/big data ETL
  • Automation, BI Report Testing & AI
  • Observability & OpenTelemetry for Pipeline Testing
  • Comprehensive Interview Prep & Career Launch
SQL, Informatica, Talend, Snowflake, ADF, AWS Glue, Python, Power BI, OpenTelemetry
View Curriculum →
Customized
⏱ Custom
★ 4.8/5

Build Your Own (Customized)

Pick exactly the modules you need from the full ETL testing module bank.
  • Choose any modules from the shared bank
  • Custom duration & pricing
  • Same trainers, same mock-interview access
  • Comprehensive Interview Prep & Career Launch included
Selected per module
View Curriculum →
LEARNING OUTCOMES

What you will be able to do

Concrete skills, not topic names — each one is something you can show an interviewer.

🎯

Design test cases that find real defects

Boundary values, equivalence classes and edge cases — not just happy-path coverage.

🧪

Automate with a real framework

Page Object Model, reusable helpers and a suite you can actually maintain.

📊

Read a defect report the way a developer will

Reproduction steps, severity and evidence — reports that get fixed, not ignored.

🔧

Build and maintain a test suite

Structure tests so they survive the product changing under them.

🔗

Test APIs, not just interfaces

Postman and REST Assured, including auth flows most courses skip.

📈

Run suites in a CI pipeline

Jenkins and GitHub Actions — tests that run on every commit automatically.

SUCCESS STORIES

Our Students Excelling Worldwide

“
Gayathri Subbiah G
Gayathri Subbiah
ETL Test Engineer
🏢 Hexaware
★★★★★

“Coming from a pure manual-testing background, the SQL depth in this course is what let me credibly claim ETL testing skills. Row-count checks, transformation logic validation, Informatica PowerCenter mapping reviews — real, employer-relevant work, not toy exercises. Hexaware's data QA team runs almost exactly this stack.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Naveen Krishnan N
Naveen Krishnan
Data QA Engineer
🏢 Persistent Systems
★★★★★

“Most ETL testing courses stop at on-prem tools like Informatica or SSIS. This one pushed into Snowflake and Azure Data Factory as well, which is where the market has actually moved. Persistent hired me specifically because I could speak to cloud-native pipeline testing, not just legacy ETL.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Shalini Ramaswamy S
Shalini Ramaswamy
QA Analyst — Data
🏢 Coforge
★★★★ ★

“The Power BI and Tableau report-testing module was unexpectedly useful — validating that a dashboard actually reflects the underlying data correctly is a whole separate skill from pipeline testing, and it's rarely taught. Good course overall; I would have liked a bit more time specifically on Tableau.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Bharath Elango B
Bharath Elango
Data Quality Engineer
🏢 LTIMindtree
★★★★★

“Being able to write my own Python and pandas scripts to validate data quality at scale, instead of manually spot-checking rows in Excel, was the module that actually changed how I work. LTIMindtree's interview had a live data-validation exercise, and this is precisely what it tested.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Revathi Sundaresan R
Revathi Sundaresan
Big Data QA Engineer
🏢 Globant
★★★★★

“Hive and Spark testing is a genuinely rare skill combination for someone coming out of a QA training program — most people learn SQL testing and stop there. Globant's big data team needed exactly that gap filled, and having real, hands-on Spark pipeline testing experience on my resume opened the door.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
TOOLS & SKILLS

Tools and skills covered

Java
Selenium
TestNG
Postman
Git
Jenkins
JMeter
SQL
🏆

Certificate of Completion

Awarded once every module and project is complete — recognised by our hiring partners.

IS THIS FOR YOU?

Who this course is for

If none of these describe you, tell us — we will point you at a better fit rather than sell you this one.

🎓

Freshers from any engineering branch

🔁

Manual testers hitting the automation ceiling

💻

Support engineers moving into QA

WHY IN FOURCUP?

Software Testing Program

Practical exposure to actual work environment

Work on live, under-construction projects with the exposure of an actual work environment, alongside a freelancer.

Dynamic syllabus structure

Built with multiple industry experts, so students become genuinely fluent in the subject rather than exam-ready.

Expert teachers who are active practitioners too

Mentors bring real industry experience and share their ups, downs and delivery skills, not just slides.

GET STARTED

Get Started Today

01
Download our Syllabus and review it thoroughly.
02
Request Call Back / Apply the Enquiry Form.
03
Join Our Free Demo Class.
04
Understand the Opportunity.
05
Join the Training session and become an expert!
KEEP EXPLORING

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CURRICULUM

Module by module

Open any module to see exactly what it covers. Nothing is hidden behind a "download the syllabus" form.

5 Weeks
DURATION
4
MODULES
4
REAL-TIME PROJECTS
5
MOCK INTERVIEWS
No prior ETL needed

ETL Testing Foundations

⏱ 5 Weeks ★ 4.7
4 modules
Most enrolled

ETL Tool Specialist (Informatica/Talend/SSIS)

⏱ 5.5 Weeks ★ 4.8
5 modules
Cloud-native

Cloud & Big Data ETL Testing

⏱ 5 Weeks ★ 4.8
5 modules
Only at Fourcup

ETL Test Automation & AI-Augmented Validation

⏱ 4 Weeks ★ 4.7
4 modules
Flagship

Complete ETL & Data Quality Flagship

⏱ 12 Weeks ★ 4.9
10 modules
Customized

Build Your Own (Customized)

⏱ Custom ★ 4.8
0 modules
1 Data Warehousing & ETL Fundamentals 1 wk ▾
TopicWhat You'll Learn
1. Data Warehouse Concepts OLTP vs. OLAP, star schema, snowflake schema, fact and dimension tables.
2. The ETL Process Extract, Transform, Load, explained the way a tester needs to reason about it: what can go wrong at each stage.
3. ETL vs. Manual/Functional Testing What's genuinely different about testing data pipelines instead of a UI or an API.
4. Requirements & Mapping Documents Reading a source-to-target mapping (STM) document, the single most important artifact in ETL testing.
4.1) Real-world starting point Most ETL bugs are found by comparing a mapping document's promise against what a query on the target table actually shows.
2 Advanced SQL for ETL Testing 2 wks ▾
TopicWhat You'll Learn
1. SQL Fundamentals for Testers SELECT, JOIN, GROUP BY, subqueries — written the way ETL validation actually uses them.
2. Data Validation Queries Row-count reconciliation, checksum/hash comparisons, and NULL/duplicate detection between source and target.
3. Advanced SQL Window functions, CTEs, and set operators (MINUS/EXCEPT) for finding exactly which rows differ between two tables.
4. Query Performance for Large Tables Writing validation queries that don't time out against a fact table with hundreds of millions of rows.
5. 🚀 Mock Interview Checkpoint SQL for ETL: reconciliation queries, live query-writing on a sample schema, and "how would you validate this transformation" questions.
3 ETL Testing Types & Techniques 1 wk ▾
TopicWhat You'll Learn
1. Data Completeness Testing Confirming every expected row made it from source to target, with none silently dropped.
2. Data Transformation Testing Verifying business rules (currency conversion, derived columns, aggregations) were applied correctly.
3. Data Quality & Data Integrity Testing Referential integrity, duplicate detection, and format/domain validation (a phone number that isn't a phone number).
4. Regression & Incremental Load Testing Verifying a scheduled incremental load doesn't reprocess or duplicate data, and that yesterday's fix didn't break today's load.
5. Metadata Testing Verifying target table structure, data types and constraints match the design, not just the data itself.
4 Comprehensive Interview Preparation & Career Launch 1 wk ▾
TopicWhat You'll Learn
1. ETL Testing Interview Question Bank SQL, data warehousing, Informatica/cloud ETL and data quality questions asked in real interviews, reviewed and explained.
2. Resume & LinkedIn Building for ETL/Data QA Roles Framing data-validation projects and SQL skills the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback Recorded, one-on-one mock interviews with a working ETL/data test engineer, with specific, actionable feedback.
4. Whiteboard-Style "Design a Test Strategy for This Pipeline" Rounds Practising the scenario question that trips up otherwise-strong candidates.
5. Salary Negotiation & Offer Evaluation Reading an offer letter and negotiating from an informed position.
6. Placement Assistance Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview A full-length, role-realistic ETL/Data Test Engineer interview, end to end.
1 Data Warehousing & ETL Fundamentals 0.5 wk ▾
TopicWhat You'll Learn
1. Data Warehouse Concepts OLTP vs. OLAP, star schema, snowflake schema, fact and dimension tables.
2. The ETL Process Extract, Transform, Load, explained the way a tester needs to reason about it: what can go wrong at each stage.
3. ETL vs. Manual/Functional Testing What's genuinely different about testing data pipelines instead of a UI or an API.
4. Requirements & Mapping Documents Reading a source-to-target mapping (STM) document, the single most important artifact in ETL testing.
4.1) Real-world starting point Most ETL bugs are found by comparing a mapping document's promise against what a query on the target table actually shows.
2 Advanced SQL for ETL Testing 1 wk ▾
TopicWhat You'll Learn
1. SQL Fundamentals for Testers SELECT, JOIN, GROUP BY, subqueries — written the way ETL validation actually uses them.
2. Data Validation Queries Row-count reconciliation, checksum/hash comparisons, and NULL/duplicate detection between source and target.
3. Advanced SQL Window functions, CTEs, and set operators (MINUS/EXCEPT) for finding exactly which rows differ between two tables.
4. Query Performance for Large Tables Writing validation queries that don't time out against a fact table with hundreds of millions of rows.
5. 🚀 Mock Interview Checkpoint SQL for ETL: reconciliation queries, live query-writing on a sample schema, and "how would you validate this transformation" questions.
3 ETL Tools — Informatica, Talend & SSIS 2 wks ▾
TopicWhat You'll Learn
1. Informatica PowerCenter Fundamentals Mappings, sessions, workflows, and reading an Informatica job log to find where a load actually failed.
2. Talend Open Studio Building and validating a simple ETL job in Talend's visual designer.
3. SQL Server Integration Services (SSIS) Reading and validating an SSIS package's data flow and control flow.
3.1) Why this module matters On-prem Informatica/SSIS environments are still common in large enterprises, especially BFSI and insurance — this module keeps that door open alongside the cloud-native modules below.
4 ETL Testing Types & Techniques 1 wk ▾
TopicWhat You'll Learn
1. Data Completeness Testing Confirming every expected row made it from source to target, with none silently dropped.
2. Data Transformation Testing Verifying business rules (currency conversion, derived columns, aggregations) were applied correctly.
3. Data Quality & Data Integrity Testing Referential integrity, duplicate detection, and format/domain validation (a phone number that isn't a phone number).
4. Regression & Incremental Load Testing Verifying a scheduled incremental load doesn't reprocess or duplicate data, and that yesterday's fix didn't break today's load.
5. Metadata Testing Verifying target table structure, data types and constraints match the design, not just the data itself.
5 Comprehensive Interview Preparation & Career Launch 1 wk ▾
TopicWhat You'll Learn
1. ETL Testing Interview Question Bank SQL, data warehousing, Informatica/cloud ETL and data quality questions asked in real interviews, reviewed and explained.
2. Resume & LinkedIn Building for ETL/Data QA Roles Framing data-validation projects and SQL skills the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback Recorded, one-on-one mock interviews with a working ETL/data test engineer, with specific, actionable feedback.
4. Whiteboard-Style "Design a Test Strategy for This Pipeline" Rounds Practising the scenario question that trips up otherwise-strong candidates.
5. Salary Negotiation & Offer Evaluation Reading an offer letter and negotiating from an informed position.
6. Placement Assistance Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview A full-length, role-realistic ETL/Data Test Engineer interview, end to end.
1 Data Warehousing & ETL Fundamentals 0.5 wk ▾
TopicWhat You'll Learn
1. Data Warehouse Concepts OLTP vs. OLAP, star schema, snowflake schema, fact and dimension tables.
2. The ETL Process Extract, Transform, Load, explained the way a tester needs to reason about it: what can go wrong at each stage.
3. ETL vs. Manual/Functional Testing What's genuinely different about testing data pipelines instead of a UI or an API.
4. Requirements & Mapping Documents Reading a source-to-target mapping (STM) document, the single most important artifact in ETL testing.
4.1) Real-world starting point Most ETL bugs are found by comparing a mapping document's promise against what a query on the target table actually shows.
2 Advanced SQL for ETL Testing 1 wk ▾
TopicWhat You'll Learn
1. SQL Fundamentals for Testers SELECT, JOIN, GROUP BY, subqueries — written the way ETL validation actually uses them.
2. Data Validation Queries Row-count reconciliation, checksum/hash comparisons, and NULL/duplicate detection between source and target.
3. Advanced SQL Window functions, CTEs, and set operators (MINUS/EXCEPT) for finding exactly which rows differ between two tables.
4. Query Performance for Large Tables Writing validation queries that don't time out against a fact table with hundreds of millions of rows.
5. 🚀 Mock Interview Checkpoint SQL for ETL: reconciliation queries, live query-writing on a sample schema, and "how would you validate this transformation" questions.
3 Big Data & Cloud ETL Testing 2 wks ▾
TopicWhat You'll Learn
1. Cloud Data Warehouses Snowflake and BigQuery fundamentals, and how validation queries differ from an on-prem warehouse.
2. Testing Azure Data Factory & AWS Glue Pipelines Validating cloud-native orchestrated ETL pipelines, not just standalone scripts.
3. Big Data Fundamentals for Testers HDFS, Hive and a working understanding of Spark jobs, enough to validate a big-data pipeline's output.
4. Testing Streaming/Near-Real-Time Pipelines What changes when data arrives continuously (Kafka-fed pipelines) instead of in a nightly batch.
4.1) Why this module matters Most ETL testing catalogues stop at on-prem Informatica; cloud and big-data pipelines are where new ETL testing roles are actually being created.
4 Observability & OpenTelemetry for Pipeline Testing 0.5 wk ▾
TopicWhat You'll Learn
1. Why Observability Matters for Pipeline Testing Moving beyond a pass/fail row-count check to understanding why a pipeline step failed or slowed down, using the same telemetry data engineers already rely on.
2. OpenTelemetry Fundamentals for Testers Traces, spans and metrics, and how a distributed trace shows a data pipeline's actual execution path across extract, transform and load stages.
3. Correlating Pipeline Failures with Trace Data Using a trace ID to jump from a failed validation query straight to the exact pipeline stage and system resource that caused it.
4. Reading Instrumented Pipeline Traces Reading Grafana/Jaeger-style trace views for a Spark or Airflow job without needing to be the engineer who wrote the pipeline.
4.1) Why this module matters 2026 hiring increasingly expects data testers to read observability data, not just query the target table — a trace often finds the root cause of a failed load faster than a support ticket ever will.
5 Comprehensive Interview Preparation & Career Launch 1 wk ▾
TopicWhat You'll Learn
1. ETL Testing Interview Question Bank SQL, data warehousing, Informatica/cloud ETL and data quality questions asked in real interviews, reviewed and explained.
2. Resume & LinkedIn Building for ETL/Data QA Roles Framing data-validation projects and SQL skills the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback Recorded, one-on-one mock interviews with a working ETL/data test engineer, with specific, actionable feedback.
4. Whiteboard-Style "Design a Test Strategy for This Pipeline" Rounds Practising the scenario question that trips up otherwise-strong candidates.
5. Salary Negotiation & Offer Evaluation Reading an offer letter and negotiating from an informed position.
6. Placement Assistance Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview A full-length, role-realistic ETL/Data Test Engineer interview, end to end.
1 Advanced SQL for ETL Testing 1 wk ▾
TopicWhat You'll Learn
1. SQL Fundamentals for Testers SELECT, JOIN, GROUP BY, subqueries — written the way ETL validation actually uses them.
2. Data Validation Queries Row-count reconciliation, checksum/hash comparisons, and NULL/duplicate detection between source and target.
3. Advanced SQL Window functions, CTEs, and set operators (MINUS/EXCEPT) for finding exactly which rows differ between two tables.
4. Query Performance for Large Tables Writing validation queries that don't time out against a fact table with hundreds of millions of rows.
5. 🚀 Mock Interview Checkpoint SQL for ETL: reconciliation queries, live query-writing on a sample schema, and "how would you validate this transformation" questions.
2 ETL Test Automation 1.5 wks ▾
TopicWhat You'll Learn
1. Automating Reconciliation with Python pandas-based scripts that compare source and target data far faster than manual SQL, run for every load.
2. Building a Lightweight ETL Test Framework Reusable comparison functions, config-driven table lists, and a results report instead of one-off scripts.
3. Scheduling & CI/CD for Data Tests Running validation automatically after every scheduled ETL job, and alerting on a mismatch before a stakeholder notices bad data.
4. Data-Driven Test Design for Pipelines Structuring test cases so adding a new table to validate doesn't mean writing new code.
3 AI-Augmented Data Validation 1 wk ▾
TopicWhat You'll Learn
1. Using AI to Generate Reconciliation Queries Turning a mapping document into a first-draft set of validation SQL queries, then reviewing it like an editor.
2. AI for Anomaly Detection in Data Sets Spotting an unusual distribution shift or outlier pattern in a large table faster than manual profiling.
3. AI-Assisted Test Data Generation for ETL Producing realistic, varied source data sets for testing transformation logic without using real production data.
3.1) Where AI gets it wrong Hallucinated column names and false-confidence anomaly calls — treating AI output as a first draft that still needs a tester's own SQL verification.
4 Comprehensive Interview Preparation & Career Launch 0.5 wk ▾
TopicWhat You'll Learn
1. ETL Testing Interview Question Bank SQL, data warehousing, Informatica/cloud ETL and data quality questions asked in real interviews, reviewed and explained.
2. Resume & LinkedIn Building for ETL/Data QA Roles Framing data-validation projects and SQL skills the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback Recorded, one-on-one mock interviews with a working ETL/data test engineer, with specific, actionable feedback.
4. Whiteboard-Style "Design a Test Strategy for This Pipeline" Rounds Practising the scenario question that trips up otherwise-strong candidates.
5. Salary Negotiation & Offer Evaluation Reading an offer letter and negotiating from an informed position.
6. Placement Assistance Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview A full-length, role-realistic ETL/Data Test Engineer interview, end to end.
1 Data Warehousing & ETL Fundamentals 1 wk ▾
TopicWhat You'll Learn
1. Data Warehouse Concepts OLTP vs. OLAP, star schema, snowflake schema, fact and dimension tables.
2. The ETL Process Extract, Transform, Load, explained the way a tester needs to reason about it: what can go wrong at each stage.
3. ETL vs. Manual/Functional Testing What's genuinely different about testing data pipelines instead of a UI or an API.
4. Requirements & Mapping Documents Reading a source-to-target mapping (STM) document, the single most important artifact in ETL testing.
4.1) Real-world starting point Most ETL bugs are found by comparing a mapping document's promise against what a query on the target table actually shows.
2 Advanced SQL for ETL Testing 2 wks ▾
TopicWhat You'll Learn
1. SQL Fundamentals for Testers SELECT, JOIN, GROUP BY, subqueries — written the way ETL validation actually uses them.
2. Data Validation Queries Row-count reconciliation, checksum/hash comparisons, and NULL/duplicate detection between source and target.
3. Advanced SQL Window functions, CTEs, and set operators (MINUS/EXCEPT) for finding exactly which rows differ between two tables.
4. Query Performance for Large Tables Writing validation queries that don't time out against a fact table with hundreds of millions of rows.
5. 🚀 Mock Interview Checkpoint SQL for ETL: reconciliation queries, live query-writing on a sample schema, and "how would you validate this transformation" questions.
3 ETL Testing Types & Techniques 1 wk ▾
TopicWhat You'll Learn
1. Data Completeness Testing Confirming every expected row made it from source to target, with none silently dropped.
2. Data Transformation Testing Verifying business rules (currency conversion, derived columns, aggregations) were applied correctly.
3. Data Quality & Data Integrity Testing Referential integrity, duplicate detection, and format/domain validation (a phone number that isn't a phone number).
4. Regression & Incremental Load Testing Verifying a scheduled incremental load doesn't reprocess or duplicate data, and that yesterday's fix didn't break today's load.
5. Metadata Testing Verifying target table structure, data types and constraints match the design, not just the data itself.
4 ETL Tools — Informatica, Talend & SSIS 1.5 wks ▾
TopicWhat You'll Learn
1. Informatica PowerCenter Fundamentals Mappings, sessions, workflows, and reading an Informatica job log to find where a load actually failed.
2. Talend Open Studio Building and validating a simple ETL job in Talend's visual designer.
3. SQL Server Integration Services (SSIS) Reading and validating an SSIS package's data flow and control flow.
3.1) Why this module matters On-prem Informatica/SSIS environments are still common in large enterprises, especially BFSI and insurance — this module keeps that door open alongside the cloud-native modules below.
5 Big Data & Cloud ETL Testing 1.5 wks ▾
TopicWhat You'll Learn
1. Cloud Data Warehouses Snowflake and BigQuery fundamentals, and how validation queries differ from an on-prem warehouse.
2. Testing Azure Data Factory & AWS Glue Pipelines Validating cloud-native orchestrated ETL pipelines, not just standalone scripts.
3. Big Data Fundamentals for Testers HDFS, Hive and a working understanding of Spark jobs, enough to validate a big-data pipeline's output.
4. Testing Streaming/Near-Real-Time Pipelines What changes when data arrives continuously (Kafka-fed pipelines) instead of in a nightly batch.
4.1) Why this module matters Most ETL testing catalogues stop at on-prem Informatica; cloud and big-data pipelines are where new ETL testing roles are actually being created.
6 ETL Test Automation 1.5 wks ▾
TopicWhat You'll Learn
1. Automating Reconciliation with Python pandas-based scripts that compare source and target data far faster than manual SQL, run for every load.
2. Building a Lightweight ETL Test Framework Reusable comparison functions, config-driven table lists, and a results report instead of one-off scripts.
3. Scheduling & CI/CD for Data Tests Running validation automatically after every scheduled ETL job, and alerting on a mismatch before a stakeholder notices bad data.
4. Data-Driven Test Design for Pipelines Structuring test cases so adding a new table to validate doesn't mean writing new code.
7 BI Report & Dashboard Testing 1 wk ▾
TopicWhat You'll Learn
1. BI Testing Fundamentals Why a report can be "technically correct" and still wrong, and how to test the layer users actually see.
2. Validating Power BI & Tableau Reports Tracing a dashboard number back to its source query and confirming it matches.
3. Filter & Drill-Down Testing Verifying interactive filters and drill-downs return the correct underlying data set, not just that they don't error.
3.1) Why this module matters Most ETL testing ends at the warehouse; the business impact of a bad number is felt in the dashboard, which is exactly where this module closes the loop.
8 AI-Augmented Data Validation 1 wk ▾
TopicWhat You'll Learn
1. Using AI to Generate Reconciliation Queries Turning a mapping document into a first-draft set of validation SQL queries, then reviewing it like an editor.
2. AI for Anomaly Detection in Data Sets Spotting an unusual distribution shift or outlier pattern in a large table faster than manual profiling.
3. AI-Assisted Test Data Generation for ETL Producing realistic, varied source data sets for testing transformation logic without using real production data.
3.1) Where AI gets it wrong Hallucinated column names and false-confidence anomaly calls — treating AI output as a first draft that still needs a tester's own SQL verification.
9 Observability & OpenTelemetry for Pipeline Testing 0.5 wk ▾
TopicWhat You'll Learn
1. Why Observability Matters for Pipeline Testing Moving beyond a pass/fail row-count check to understanding why a pipeline step failed or slowed down, using the same telemetry data engineers already rely on.
2. OpenTelemetry Fundamentals for Testers Traces, spans and metrics, and how a distributed trace shows a data pipeline's actual execution path across extract, transform and load stages.
3. Correlating Pipeline Failures with Trace Data Using a trace ID to jump from a failed validation query straight to the exact pipeline stage and system resource that caused it.
4. Reading Instrumented Pipeline Traces Reading Grafana/Jaeger-style trace views for a Spark or Airflow job without needing to be the engineer who wrote the pipeline.
4.1) Why this module matters 2026 hiring increasingly expects data testers to read observability data, not just query the target table — a trace often finds the root cause of a failed load faster than a support ticket ever will.
10 Comprehensive Interview Preparation & Career Launch 1 wk ▾
TopicWhat You'll Learn
1. ETL Testing Interview Question Bank SQL, data warehousing, Informatica/cloud ETL and data quality questions asked in real interviews, reviewed and explained.
2. Resume & LinkedIn Building for ETL/Data QA Roles Framing data-validation projects and SQL skills the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback Recorded, one-on-one mock interviews with a working ETL/data test engineer, with specific, actionable feedback.
4. Whiteboard-Style "Design a Test Strategy for This Pipeline" Rounds Practising the scenario question that trips up otherwise-strong candidates.
5. Salary Negotiation & Offer Evaluation Reading an offer letter and negotiating from an informed position.
6. Placement Assistance Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview A full-length, role-realistic ETL/Data Test Engineer interview, end to end.

The curriculum for this package has not been added yet.

WHAT YOU GET

This Course Includes

💻

Online

🎯

11 Weeks Duration (Flexible)

🖥

4 Real Industry Projects

🏆

Certificate on Completion

♾️

Lifetime Access to Recordings

💼

Job References to 50+ Companies

🎯

5 Mock Interview Rounds

🗣

Soft Skills & Communication

📱

WhatsApp Batch Support

📥

Brochure & Syllabus Download

🛠 Tools Covered:
SQLInformatica PowerCenterTalendSSISSnowflakeAzure Data FactoryAWS GlueHiveSparkPythonpandasPower BITableauJIRAChatGPT/Claude
⬇️ Download Syllabus 💬 Enquire About This Course
SUCCESS STORIES

Our Students Excelling Worldwide

“
Gayathri Subbiah G
Gayathri Subbiah
ETL Test Engineer
🏢 Hexaware
★★★★★

“Coming from a pure manual-testing background, the SQL depth in this course is what let me credibly claim ETL testing skills. Row-count checks, transformation logic validation, Informatica PowerCenter mapping reviews — real, employer-relevant work, not toy exercises. Hexaware's data QA team runs almost exactly this stack.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Naveen Krishnan N
Naveen Krishnan
Data QA Engineer
🏢 Persistent Systems
★★★★★

“Most ETL testing courses stop at on-prem tools like Informatica or SSIS. This one pushed into Snowflake and Azure Data Factory as well, which is where the market has actually moved. Persistent hired me specifically because I could speak to cloud-native pipeline testing, not just legacy ETL.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Shalini Ramaswamy S
Shalini Ramaswamy
QA Analyst — Data
🏢 Coforge
★★★★ ★

“The Power BI and Tableau report-testing module was unexpectedly useful — validating that a dashboard actually reflects the underlying data correctly is a whole separate skill from pipeline testing, and it's rarely taught. Good course overall; I would have liked a bit more time specifically on Tableau.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Bharath Elango B
Bharath Elango
Data Quality Engineer
🏢 LTIMindtree
★★★★★

“Being able to write my own Python and pandas scripts to validate data quality at scale, instead of manually spot-checking rows in Excel, was the module that actually changed how I work. LTIMindtree's interview had a live data-validation exercise, and this is precisely what it tested.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Revathi Sundaresan R
Revathi Sundaresan
Big Data QA Engineer
🏢 Globant
★★★★★

“Hive and Spark testing is a genuinely rare skill combination for someone coming out of a QA training program — most people learn SQL testing and stop there. Globant's big data team needed exactly that gap filled, and having real, hands-on Spark pipeline testing experience on my resume opened the door.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
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YOUR MENTOR

Who teaches this course

Sessions are taught by someone who does this work, not by a full-time trainer reading slides.

👨‍🏫

Fourcup Data Quality Faculty

Real Industry Trainers, Not Just Instructors

Every Fourcup trainer has a minimum of 8+ years of total IT industry experience and 5+ years of specific, hands-on experience in the exact tool or domain they teach. They are currently working professionals — the frameworks, edge cases and interview questions taught are what's actually used in live projects today. Mock-interview mentors are the same trainers who conduct real technical interviews at their day jobs.

8+ years experience Active practitioner
SUCCESS STORIES

Our Students Excelling Worldwide

“
Gayathri Subbiah G
Gayathri Subbiah
ETL Test Engineer
🏢 Hexaware
★★★★★

“Coming from a pure manual-testing background, the SQL depth in this course is what let me credibly claim ETL testing skills. Row-count checks, transformation logic validation, Informatica PowerCenter mapping reviews — real, employer-relevant work, not toy exercises. Hexaware's data QA team runs almost exactly this stack.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Naveen Krishnan N
Naveen Krishnan
Data QA Engineer
🏢 Persistent Systems
★★★★★

“Most ETL testing courses stop at on-prem tools like Informatica or SSIS. This one pushed into Snowflake and Azure Data Factory as well, which is where the market has actually moved. Persistent hired me specifically because I could speak to cloud-native pipeline testing, not just legacy ETL.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Shalini Ramaswamy S
Shalini Ramaswamy
QA Analyst — Data
🏢 Coforge
★★★★ ★

“The Power BI and Tableau report-testing module was unexpectedly useful — validating that a dashboard actually reflects the underlying data correctly is a whole separate skill from pipeline testing, and it's rarely taught. Good course overall; I would have liked a bit more time specifically on Tableau.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Bharath Elango B
Bharath Elango
Data Quality Engineer
🏢 LTIMindtree
★★★★★

“Being able to write my own Python and pandas scripts to validate data quality at scale, instead of manually spot-checking rows in Excel, was the module that actually changed how I work. LTIMindtree's interview had a live data-validation exercise, and this is precisely what it tested.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Revathi Sundaresan R
Revathi Sundaresan
Big Data QA Engineer
🏢 Globant
★★★★★

“Hive and Spark testing is a genuinely rare skill combination for someone coming out of a QA training program — most people learn SQL testing and stop there. Globant's big data team needed exactly that gap filled, and having real, hands-on Spark pipeline testing experience on my resume opened the door.”

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BATCHES

When you can start

Weekday, weekend and fast-track options. Seats are limited so sessions stay small enough to ask questions in.

Batch dates for this course have not been added yet.

SUCCESS STORIES

Our Students Excelling Worldwide

“
Gayathri Subbiah G
Gayathri Subbiah
ETL Test Engineer
🏢 Hexaware
★★★★★

“Coming from a pure manual-testing background, the SQL depth in this course is what let me credibly claim ETL testing skills. Row-count checks, transformation logic validation, Informatica PowerCenter mapping reviews — real, employer-relevant work, not toy exercises. Hexaware's data QA team runs almost exactly this stack.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Naveen Krishnan N
Naveen Krishnan
Data QA Engineer
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★★★★★

“Most ETL testing courses stop at on-prem tools like Informatica or SSIS. This one pushed into Snowflake and Azure Data Factory as well, which is where the market has actually moved. Persistent hired me specifically because I could speak to cloud-native pipeline testing, not just legacy ETL.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Shalini Ramaswamy S
Shalini Ramaswamy
QA Analyst — Data
🏢 Coforge
★★★★ ★

“The Power BI and Tableau report-testing module was unexpectedly useful — validating that a dashboard actually reflects the underlying data correctly is a whole separate skill from pipeline testing, and it's rarely taught. Good course overall; I would have liked a bit more time specifically on Tableau.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Bharath Elango B
Bharath Elango
Data Quality Engineer
🏢 LTIMindtree
★★★★★

“Being able to write my own Python and pandas scripts to validate data quality at scale, instead of manually spot-checking rows in Excel, was the module that actually changed how I work. LTIMindtree's interview had a live data-validation exercise, and this is precisely what it tested.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Revathi Sundaresan R
Revathi Sundaresan
Big Data QA Engineer
🏢 Globant
★★★★★

“Hive and Spark testing is a genuinely rare skill combination for someone coming out of a QA training program — most people learn SQL testing and stop there. Globant's big data team needed exactly that gap filled, and having real, hands-on Spark pipeline testing experience on my resume opened the door.”

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FAQ

Questions people actually ask

If yours is not here, call us — a working engineer will answer, not a call centre.

No questions have been added for this course yet.

SUCCESS STORIES

Our Students Excelling Worldwide

“
Gayathri Subbiah G
Gayathri Subbiah
ETL Test Engineer
🏢 Hexaware
★★★★★

“Coming from a pure manual-testing background, the SQL depth in this course is what let me credibly claim ETL testing skills. Row-count checks, transformation logic validation, Informatica PowerCenter mapping reviews — real, employer-relevant work, not toy exercises. Hexaware's data QA team runs almost exactly this stack.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Naveen Krishnan N
Naveen Krishnan
Data QA Engineer
🏢 Persistent Systems
★★★★★

“Most ETL testing courses stop at on-prem tools like Informatica or SSIS. This one pushed into Snowflake and Azure Data Factory as well, which is where the market has actually moved. Persistent hired me specifically because I could speak to cloud-native pipeline testing, not just legacy ETL.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Shalini Ramaswamy S
Shalini Ramaswamy
QA Analyst — Data
🏢 Coforge
★★★★ ★

“The Power BI and Tableau report-testing module was unexpectedly useful — validating that a dashboard actually reflects the underlying data correctly is a whole separate skill from pipeline testing, and it's rarely taught. Good course overall; I would have liked a bit more time specifically on Tableau.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Bharath Elango B
Bharath Elango
Data Quality Engineer
🏢 LTIMindtree
★★★★★

“Being able to write my own Python and pandas scripts to validate data quality at scale, instead of manually spot-checking rows in Excel, was the module that actually changed how I work. LTIMindtree's interview had a live data-validation exercise, and this is precisely what it tested.”

🎓 Complete ETL & Data Quality Testing — SQL, Cloud & AI
“
Revathi Sundaresan R
Revathi Sundaresan
Big Data QA Engineer
🏢 Globant
★★★★★

“Hive and Spark testing is a genuinely rare skill combination for someone coming out of a QA training program — most people learn SQL testing and stop there. Globant's big data team needed exactly that gap filled, and having real, hands-on Spark pipeline testing experience on my resume opened the door.”

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